Flush Job API
editFlush Job API
editThe Flush Job API provides the ability to flush a machine learning job’s
datafeed in the cluster.
It accepts a FlushJobRequest
object and responds
with a FlushJobResponse
object.
Flush Job Request
editA FlushJobRequest
object gets created with an existing non-null jobId
.
All other fields are optional for the request.
Optional Arguments
editThe following arguments are optional.
flushJobRequest.setCalcInterim(true); flushJobRequest.setAdvanceTime("2018-08-31T16:35:07+00:00"); flushJobRequest.setStart("2018-08-31T16:35:17+00:00"); flushJobRequest.setEnd("2018-08-31T16:35:27+00:00"); flushJobRequest.setSkipTime("2018-08-31T16:35:00+00:00");
Set request to calculate the interim results |
|
Set the advanced time to flush to the particular time value |
|
Set the start time for the range of buckets on which
to calculate the interim results (requires |
|
Set the end time for the range of buckets on which
to calculate interim results (requires |
|
Set the skip time to skip a particular time value |
Flush Job Response
editA FlushJobResponse
contains an acknowledgement and an optional end date for the
last finalized bucket
Synchronous Execution
editWhen executing a FlushJobRequest
in the following manner, the client waits
for the FlushJobResponse
to be returned before continuing with code execution:
FlushJobResponse flushJobResponse = client.machineLearning().flushJob(flushJobRequest, RequestOptions.DEFAULT);
Asynchronous Execution
editExecuting a FlushJobRequest
can also be done in an asynchronous fashion so that
the client can return directly. Users need to specify how the response or
potential failures will be handled by passing the request and a listener to the
asynchronous flush-job method:
The asynchronous method does not block and returns immediately. Once it is
completed the ActionListener
is called back using the onResponse
method
if the execution successfully completed or using the onFailure
method if
it failed.
A typical listener for flush-job
looks like: